UTexas

Business Analytics (BAX)

BAX 304  Introduction to Problem Solving and Programming  3 Hours  

3 Lecture Hours  0 Lab Hours  
Programming skills for creating easy-to-maintain systems for business applications. Object-oriented and structured methodologies with Python. Only one of the following may be counted: Business Analytics 304, 305, or Management Information Systems 304. Three lecture hours a week for one semester.
Same as: MIS 304  
Restrictions: Restricted to students in the McCombs School of Business.  
Grading: Letter Grade  
Repeatable for credit: No  
Academic Level: Lower Division  

BAX 305  Programming for Data Analytics  3 Hours  

3 Lecture Hours  0 Lab Hours  
Examine general principles of computer languages and basic object-oriented programming principles. Translate business problems from English into programs written using the Python language. Explore automatic function optimization needed for decision-making. Utilize Python for hands-on work. Only one of the following may be counted: Business Analytics 304, 305, or Management Information Systems 304. Three lecture hours a week for one semester.
Restrictions: Restricted to students in the McCombs School of Business  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Lower Division  

BAX 119S, 219S, 319S, 419S, 519S, 619S, 719S, 819S, 919S  Topics in Business Analytics  1-9 Hours  

0 Lecture Hours  0 Lab Hours  
This course is used to record credit the student earns while enrolled at another institution in a program administered by the University's Study Abroad Office. Credit is recorded as assigned by the study abroad advisor in the academic unit. University credit is awarded for work in an exchange program; it may be counted as coursework taken in residence. Transfer credit is awarded for work in an affiliated studies program.
Grading: Student Option  
Repeatable for credit: May be repeated for credit when the topics vary.  
Academic Level: Lower Division  

BAX 320F  Foundations of Business Analytics  3 Hours  

3 Lecture Hours  0 Lab Hours  
Three lecture hours a week for one semester.
Restrictions: Restricted to non-McCombs School of Business majors.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 325  Database Management  3 Hours  

3 Lecture Hours  0 Lab Hours  
Beginning and intermediate topics in data modeling for relational database management systems. Only one of the following may be counted: Business Analytics 325, 327, or Management Information Systems 325. Three lecture hours a week for one semester.
Same as: MIS 325  
Restrictions: Restricted to students in the McCombs School of Business.  
Grading: Letter Grade  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 327  Data Management  3 Hours  

3 Lecture Hours  0 Lab Hours  
Explore how to tailor data strategy to business strategy and the technical skills related to understanding data sources, acquiring data, storing data efficiently, and processing the data in preparation for analysis. Learn how to engineer and manage data pipelines, create data stores for analytics, and use data management specific APIs (application programming interfaces). Examine the importance of implementing data governance, understanding data limitations, and assessing the fairness of data collection and usage. Only one of the following may be counted: Business Analytics 325, 327, Management Information Systems 325. Three lecture hours a week for one semester.
Restrictions: Restricted to students in the McCombs School of Business  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 129S, 229S, 329S, 429S, 529S, 629S, 729S, 829S, 929S  Topics in Business Analytics  1-9 Hours  

0 Lecture Hours  0 Lab Hours  
This course is used to record credit the student earns while enrolled at another institution in a program administered by the University's Study Abroad Office. Credit is recorded as assigned by the study abroad advisor in the academic unit. University credit is awarded for work in an exchange program; it may be counted as coursework taken in residence. Transfer credit is awarded for work in an affiliated studies program.
Grading: Student Option  
Repeatable for credit: May be repeated for credit when the topics vary.  
Academic Level: Upper Division  

BAX 332F  Topics in Foundations of Business Analytics  3 Hours  

3 Lecture Hours  0 Lab Hours  
Three lecture hours a week for one semester.
Restrictions: Restricted to non-McCombs School of Business majors.  
Grading: Student Option  
Repeatable for credit: May be repeated for credit when the topics vary.  
Academic Level: Upper Division  

BAX 338  Supply Chain Modeling and Optimization  3 Hours  

3 Lecture Hours  0 Lab Hours  
Framing, formulating, and applying quantitative optimization and descriptive models to support supply chain and operations management decisions, using spreadsheets and other software. Requires familiarity with spreadsheets. Only one of the following may be counted: Operations Management 337 (Topic 2), 338, Business Analytics 338. Three lecture hours a week for one semester.
Same as: O M 338  
Pre/Corequisites: Operations Management 334M, 235, or 235H.  
Restrictions: Restricted to students in a business major.  
Grading: Letter Grade  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 357  Predictive Analytics  3 Hours  

3 Lecture Hours  0 Lab Hours  
Introduction to machine learning and artificial intelligence techniques with a focus on business applications and decision-making. Examine predictive frameworks, including tree-based techniques and artificial neural networks and their applications in business contexts. Explore solid foundations for evaluating models to understand their impact in a given business context, discuss issues related to algorithmic decision-making, and algorithmic bias and fairness. Only one of the following may be counted: Business Analytics 357, 372 (Topic 2), Management Information Systems 373 (Topic 17), Marketing 372 (Topic: Predictive Analytics and Data Mining), 372 (Topic 22). Three lecture hours a week for one semester.
Pre/Corequisites: Upper-division standing; Statistics 301, 301H, 309, or 309H; and Business Analytics 304, 305, or Management Information Systems 304.  
Restrictions: Restricted to students in the McCombs School of Business  
Grading: Letter Grade  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 358  Optimization Methods and Decision Making  3 Hours  

3 Lecture Hours  0 Lab Hours  
Examine decision-making in deterministic settings. Discuss optimization and planning in various contexts including portfolio selection, production planning, marketing allocations, revenue management and pricing. Explore linear, quadratic, nonlinear, and integer programming. Utilize Python for hands-on work. Three lecture hours a week for one semester.
Pre/Corequisites: Upper-division standing; Business Analytics 304 or Management Information Systems 304; Statistics 235 or 235H; and Decision Sciences 235 or 235H.  
Restrictions: Restricted to students in the McCombs School of Business  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 360, 460  Information and Analysis  3-4 Hours  

3,4 Lecture Hours  0 Lab Hours  
The development and analysis of information for marketing management sources. Marketing 360, 460 and Business Analytics 360, 460 may not both be counted. For each semester hour of credit earned, one lecture hour a week for one semester.
Same as: MKT 360, MKT 460  
Pre/Corequisites: Marketing 337, 337H, or 337N; and Statistics 301, 301H, 309 or 309H.  
Restrictions: Restricted to students in a business major.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 362  Auditing and Control  3 Hours  

3 Lecture Hours  0 Lab Hours  
Professional practice standards and procedures of auditing: ethics, legal liability, sampling methods, control systems, control design, and control evaluation. Only one of the following may be counted: Accounting 358C, 362, 380K (Topic 4), Business Analytics 362. Three lecture hours a week for one semester.
Same as: ACC 362  
Pre/Corequisites: Accounting 311 or 311H with a grade of at least C-; and Accounting 312 or 312H.  
Restrictions: Restricted to students in a business major.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 365  Ethics of Business Analytics  3 Hours  

3 Lecture Hours  0 Lab Hours  
Analyze the ethical questions and implications of applying data science to business problems. Examine ethical, policy, and legal terminology used to conceptualize challenges. Explore technical approaches to characterize ethical problems, and historical cases and recent controversies involving data science ethics. Discuss data as abstraction and representations, data collection, consent and privacy, bias in predictive modeling, and algorithms as actors and feedback loops. Three lecture hours a week for one semester.
Restrictions: Restricted to students in a business major.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 366  Advanced Programming for Data Analytics  3 Hours  

3 Lecture Hours  0 Lab Hours  
Study exploratory data analysis in-depth. Examine machine learning approaches such as classification and clustering, with a focus on practical implementation of machine learning projects. Explore tools for extraction of structured data and methods for analyzing data networks. Utilize Python for hands-on work. Three lecture hours a week for one semester.
Restrictions: Restricted to students in a business major.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 366P  Business Analytics Practicum  3 Hours  

3 Lecture Hours  0 Lab Hours  
Apply skills in a major area and focus on additional business analytics skills through group projects conducted in a professional setting. Work with a private or a public enterprise. Three lecture hours a week for one semester.
Pre/Corequisites: A minimum of forty-five semester hours of college coursework.  
Restrictions: Restricted to students in a business major.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 367  Advanced Predictive Analytics  3 Hours  

3 Lecture Hours  0 Lab Hours  
Explore problem formulation, data understanding and preparation, and class imbalance and biases in observed and unobserved information. Examine predictive frameworks such as network and graph data analysis, unstructured data analysis, and subjects related to deep neural network architectures and their business applications. Three lecture hours a week for one semester.
Restrictions: Restricted to students in a business major.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 368  Advanced Optimization Methods and Decision Making  3 Hours  

3 Lecture Hours  0 Lab Hours  
Examine decision-making under uncertainty. Discuss optimization and planning in various contexts including queueing, resource allocation, pricing, inventory management, and artificial intelligence. Explore stochastic optimization, sample average approximations, simulation, and sequential decision making. Utilize Python for hands-on work. Three lecture hours a week for one semester.
Restrictions: Restricted to students in a business major.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 171T  Topics in Business Analytics  1 Hour  

1 Lecture Hours  0 Lab Hours  
One lecture hour a week for one semester.
Grading: Student Option  
Repeatable for credit: May be repeated for credit when the topics vary.  
Academic Level: Upper Division  

BAX 372T  Topics in Business Analytics  3 Hours  

3 Lecture Hours  0 Lab Hours  
Three lecture hours a week for one semester.
Pre/Corequisites: Prerequisites vary with the topic.  
Restrictions: Restricted to students in a business major.  
Grading: Student Option  
Repeatable for credit: May be repeated for credit when the topics vary.  
Academic Level: Upper Division  
Topic 11: Advanced Analytics Programming. Focus on data analysis using the Python programming language. Explore machine learning tasks such as classification and clustering. Only one of the following may be counted: Business Analytics 372 (Topic 1), 372T (Topic 11), Management Information Systems 372T (Topic 11), 373 (Topic 11). Same as: MIS 372T.11 Additional prerequisite: Business Analytics 304 or Management Information Systems 304. Restrictions: Restricted to students in a business major.

Topic 13: Data Driven Marketing. Explore marketing research analytics using various types of data analytics and statistical learning modes. Only one of the following may be counted: Business Analytics 372 (Topic 10), 372T (Topic 13), Marketing 372 (Topic 25), 372T (Topic 13). Same as: MKT 372T.13

Topic 14: Quantitative Investment Management. Examine the economic, statistical, and computing frameworks behind quantitative approaches to evaluating and implementing investment strategies, as well as the empirical evidence on their performance in multiple asset classes. Apply the quantitative big-data approach by developing, pitching, and backtesting an original trading strategy. Only one of the following may be counted: Business Analytics 372 (Topic 14), 372T (Topic 14), Finance 372 (Topic: Quant Investment Management), 372 (Topic 4), 372T (Topic 14). Same as: FIN 372T.14 Additional prerequisite: Finance 367. Restrictions: Restricted to students in a business major.

Topic 15: Financial Technology. Explore an overview of the most recent technological advances that are changing the financial services industry and how these technological breakthroughs offer new ways for people to save, invest, borrow, and transact. Analyze how new technologies create value in the financial industry, from reducing unit cost, increasing transparency, increasing competition, creating network effects, leveraging economies of scales, and lowering asymmetric information. Examine the competitive landscape and the market opportunities and threats for incumbents and new entrants. Only one of the following may be counted: Business Analytics 372 (Topic 5), 372T (Topic 15), Finance 372 (Topic: Financial Technology), 372 (Topic 5), 372T (Topic 12). Same as: FIN 372T.15 Restrictions: Restricted to students in a business major.

Topic 16: Optimization Method in Finance. Explore quantitative methods and techniques in optimization and simulation, and their use in financial decision making. Discuss theory and application in portfolio selection, options and other derivative pricing, index tracking, risk measures, volatility estimating. Examine linear, quadratic, nonlinear, and integer programming; dynamic programming; robust optimization; Monte Carlo methods and variance reduction techniques. Emphasis will be placed on problem solving with advanced computational programming languages. Only one of the following may be counted: Business Analytics 372 (Topic 6), 372T (Topic 16), Decision Science 372 (Topic 6), 372T (Topic 16), Finance 372 (Topic: Optimization Method Finance), 372 (Topic 6), 372T (Topic 16), Statistics 372 (Topic 6), 372T (Topic 16). Same as: D S 372T.16, FIN 372T.16, STA 372T.16

Topic 17: Health Care Analytics. Explore key management challenges and how data may be leveraged to guide decisions and improve operations, with the unifying theme of providing health care services in a manner that leads to lower cost and higher quality. Only one of the following may be counted: Business Analytics 372 (Topic 17), 372T (Topic 17), Management Information Systems 373 (Topic 26), 372T (Topic 17) Operations Management 337 (Topic 8), 372T (Topic 17). Same as: MIS 372T.17, O M 372T.17

Topic 18: People Analytics. Explore the use of analytics in the creation and management of human capital. Examine recruiting, selecting, deploying, developing, and managing performance of employees. Only one of the following may be counted: Business Analytics 372 (Topic 7), 372T (Topic 18), Management 337 (Topic: People Analytics), 337 (Topic 7), 372T (Topic 18). Same as: MAN 372T.18

Topic 19: Pricing and Channels. Explore the concept, theory, and latest thinking that bares on key issues in pricing and channels; taking the perspective of the marketing manager. Apply concepts and theory, through extensive case analyses, to solve pricing and channel problems in realistic business settings. Only one of the following may be counted: Business Analytics 372 (Topic 8), 372T (Topic 19), Marketing 372 (Topic 14), 372T (Topic 19). Same as: MKT 372T.19

Topic 20: Supply Chain Analytics. Study dynamic demand forecasting models based on both data aggregation as well as hierarchical aggregation of point-of-sale predictive analytics. Explore the use of developed predictive dynamic models for operations planning and operations decision maing. Only one of the following may be counted: Business Analytics 372 (Topic 16), 372T (Topic 20), Operations Management 337 (Topic 6), 372T (Topic 20). Same as: O M 372T.20 Additional prerequisite: Credit or registration for Operations Management 334M, 235 or 235H.

Topic 21: Time Series Forecasting. Examine statistical forecasting methods used in business. Discuss Box-Jenkins models; exponential smoothing models; ARCH/GARCH models for varying volatility in financial returns; seasonal adjustment of time series; tests for nonstationary of time series; and modeling multiple time series. Only one of the following may be counted: Business Analytics 372 (Topic 21), 372T (Topic 21), Operations Management 372T (Topic: Time Series Forecasting), 372T (Topic 21), Statistics 372 (Topic 9), 372T (Topic 21). Same as: O M 372T.21, STA 372T.21 Additional prerequisite: Statistics 235, 235H, Statistics and Data Sciences 358, Economics 341K, or other course with basic knowledge about regression.

Topic 22: Predictive Analytics and Data Mining. Introduction to data mining problems and tools to enhance managerial decision making at all levels of the organization. Discuss scenarios, including the use of data mining to support customer relationship management (CRM) decisions, decisions in the entertainment industry, financial trading, and even professional sports teams. Only one of the following may be counted: Business Analytics 372 (Topic 2), 372T (Topic 22), Management Information Systems 373 (Topic 17), 372T (Topic 22), Marketing 372 (Topic 22), 372T (Topic 22). Same as: MIS 372T.22, MKT 372T.22 Additional prerequisite: Statistics 301 or 301H.

Topic 23: Social Media Analytics. Introduction to social network analysis for business value using statistical optimization and decision theory. Analyze online search and conversation data for market sensing, sentiments, product quality, reputation, recommendations, and brand awareness. Only one of the following may be counted: Business Analytics 372 (Topic 23), 372T (Topic 23), Management Information Systems 372T (Topic 23), 373 (Topic 23). Same as: MIS 372T.23 Restrictions: Restricted to students in a business major.

Topic 24: Data-Driven Revenue Management. Use data-driven tactics and strategies to predict consumer demand and optimize price and capacity/inventory availability to maximize revenue or profit. Explore demand analytics and revenue management techniques, produce rigorous and practical insights, and develop data analysis and optimization skills, based on business cases, datasets, and exercises. Examine diverse contexts and industries, such as conventional retailing, e-commerce, airline/hotel, and contemporary platform and cloud services (e.g., Uber, Dropbox, Netflix, AWS), by utilizing software (Excel, R, or Python) to build models, conduct optimization, and develop insights for a variety of revenue management examples. Only one of the following may be counted: Business Analytics 372T (Topic 24), Management Information Systems 372T (Topic 24), Operations Management 372T (Topic 24). Same as: MIS 372T.24, O M 372T.24

Topic 25: User Generated Content Analytics. Focus on a gamut of questions ranging from strategic to operational matters pertaining to a firm's social media initiatives, metrics to capture relevant outcomes, and predictive analysis to link social media chatter to business performance. Only one of the following may be counted: Business Analytics 372 (Topic 4), 372T (Topic 25), Management Information Systems 372T (Topic 25), 373 (Topic 25). Same as: MIS 372T.25

Topic 26: Data Analytics for Marketing. Introduction to making more effective marketing decisions through the use of data. Examine sources, collecting and cleansing methods, conducting analyses, and presenting of data. Use real-world data and applications from a variety of industries. Only one of the following may be counted: Business Analytics 372 (Topic 9), 372T (Topic 26), Marketing 354 (Topic 26), 372 (Topic 23). Same as: MKT 354.26
  

BAX 372T.11  Advanced Analytics Programming  3 Hours  

3 Lecture Hours  0 Lab Hours  
Focus on data analysis using the Python programming language. Explore machine learning tasks such as classification and clustering. Only one of the following may be counted: Business Analytics 372 (Topic 1), 372T (Topic 11), Management Information Systems 372T (Topic 11), 373 (Topic 11).
Same as: MIS 372T.11  
Pre/Corequisites: Business Analytics 304 or Management Information Systems 304.  
Restrictions: Restricted to students in a business major.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 372T.13  Data Driven Marketing  3 Hours  

3 Lecture Hours  0 Lab Hours  
Explore marketing research analytics using various types of data analytics and statistical learning modes. Only one of the following may be counted: Business Analytics 372 (Topic 10), 372T (Topic 13), Marketing 372 (Topic 25), 372T (Topic 13).
Same as: MKT 372T.13  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 372T.14  Quantitative Investment Management  3 Hours  

3 Lecture Hours  0 Lab Hours  
Examine the economic, statistical, and computing frameworks behind quantitative approaches to evaluating and implementing investment strategies, as well as the empirical evidence on their performance in multiple asset classes. Apply the quantitative big-data approach by developing, pitching, and backtesting an original trading strategy. Only one of the following may be counted: Business Analytics 372 (Topic 14), 372T (Topic 14), Finance 372 (Topic: Quant Investment Management), 372 (Topic 4), 372T (Topic 14).
Same as: FIN 372T.14  
Pre/Corequisites: Finance 367.  
Restrictions: Restricted to students in a business major.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 372T.15  Financial Technology  3 Hours  

3 Lecture Hours  0 Lab Hours  
Explore an overview of the most recent technological advances that are changing the financial services industry and how these technological breakthroughs offer new ways for people to save, invest, borrow, and transact. Analyze how new technologies create value in the financial industry, from reducing unit cost, increasing transparency, increasing competition, creating network effects, leveraging economies of scales, and lowering asymmetric information. Examine the competitive landscape and the market opportunities and threats for incumbents and new entrants. Only one of the following may be counted: Business Analytics 372 (Topic 5), 372T (Topic 15), Finance 372 (Topic: Financial Technology), 372 (Topic 5), 372T (Topic 12).
Same as: FIN 372T.15  
Restrictions: Restricted to students in a business major.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 372T.16  Optimization Method in Finance  3 Hours  

3 Lecture Hours  0 Lab Hours  
Explore quantitative methods and techniques in optimization and simulation, and their use in financial decision making. Discuss theory and application in portfolio selection, options and other derivative pricing, index tracking, risk measures, volatility estimating. Examine linear, quadratic, nonlinear, and integer programming; dynamic programming; robust optimization; Monte Carlo methods and variance reduction techniques. Emphasis will be placed on problem solving with advanced computational programming languages. Only one of the following may be counted: Business Analytics 372 (Topic 6), 372T (Topic 16), Decision Science 372 (Topic 6), 372T (Topic 16), Finance 372 (Topic: Optimization Method Finance), 372 (Topic 6), 372T (Topic 16), Statistics 372 (Topic 6), 372T (Topic 16).
Same as: D S 372T.16, FIN 372T.16, STA 372T.16  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 372T.17  Health Care Analytics  3 Hours  

3 Lecture Hours  0 Lab Hours  
Explore key management challenges and how data may be leveraged to guide decisions and improve operations, with the unifying theme of providing health care services in a manner that leads to lower cost and higher quality. Only one of the following may be counted: Business Analytics 372 (Topic 17), 372T (Topic 17), Management Information Systems 373 (Topic 26), 372T (Topic 17) Operations Management 337 (Topic 8), 372T (Topic 17).
Same as: MIS 372T.17, O M 372T.17  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 372T.18  People Analytics  3 Hours  

3 Lecture Hours  0 Lab Hours  
Explore the use of analytics in the creation and management of human capital. Examine recruiting, selecting, deploying, developing, and managing performance of employees. Only one of the following may be counted: Business Analytics 372 (Topic 7), 372T (Topic 18), Management 337 (Topic: People Analytics), 337 (Topic 7), 372T (Topic 18).
Same as: MAN 372T.18  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 372T.19  Pricing and Channels  3 Hours  

3 Lecture Hours  0 Lab Hours  
Explore the concept, theory, and latest thinking that bares on key issues in pricing and channels; taking the perspective of the marketing manager. Apply concepts and theory, through extensive case analyses, to solve pricing and channel problems in realistic business settings. Only one of the following may be counted: Business Analytics 372 (Topic 8), 372T (Topic 19), Marketing 372 (Topic 14), 372T (Topic 19).
Same as: MKT 372T.19  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 372T.20  Supply Chain Analytics  3 Hours  

3 Lecture Hours  0 Lab Hours  
Study dynamic demand forecasting models based on both data aggregation as well as hierarchical aggregation of point-of-sale predictive analytics. Explore the use of developed predictive dynamic models for operations planning and operations decision maing. Only one of the following may be counted: Business Analytics 372 (Topic 16), 372T (Topic 20), Operations Management 337 (Topic 6), 372T (Topic 20).
Same as: O M 372T.20  
Pre/Corequisites: Credit or registration for Operations Management 334M, 235 or 235H.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 372T.21  Time Series Forecasting  3 Hours  

3 Lecture Hours  0 Lab Hours  
Examine statistical forecasting methods used in business. Discuss Box-Jenkins models; exponential smoothing models; ARCH/GARCH models for varying volatility in financial returns; seasonal adjustment of time series; tests for nonstationary of time series; and modeling multiple time series. Only one of the following may be counted: Business Analytics 372 (Topic 21), 372T (Topic 21), Operations Management 372T (Topic: Time Series Forecasting), 372T (Topic 21), Statistics 372 (Topic 9), 372T (Topic 21).
Same as: O M 372T.21, STA 372T.21  
Pre/Corequisites: Statistics 235, 235H, Statistics and Data Sciences 358, Economics 341K, or other course with basic knowledge about regression.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 372T.22  Predictive Analytics and Data Mining  3 Hours  

3 Lecture Hours  0 Lab Hours  
Introduction to data mining problems and tools to enhance managerial decision making at all levels of the organization. Discuss scenarios, including the use of data mining to support customer relationship management (CRM) decisions, decisions in the entertainment industry, financial trading, and even professional sports teams. Only one of the following may be counted: Business Analytics 372 (Topic 2), 372T (Topic 22), Management Information Systems 373 (Topic 17), 372T (Topic 22), Marketing 372 (Topic 22), 372T (Topic 22).
Same as: MIS 372T.22, MKT 372T.22  
Pre/Corequisites: Statistics 301 or 301H.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 372T.23  Social Media Analytics  3 Hours  

3 Lecture Hours  0 Lab Hours  
Introduction to social network analysis for business value using statistical optimization and decision theory. Analyze online search and conversation data for market sensing, sentiments, product quality, reputation, recommendations, and brand awareness. Only one of the following may be counted: Business Analytics 372 (Topic 23), 372T (Topic 23), Management Information Systems 372T (Topic 23), 373 (Topic 23).
Same as: MIS 372T.23  
Restrictions: Restricted to students in a business major.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 372T.24  Data-Driven Revenue Management  3 Hours  

3 Lecture Hours  0 Lab Hours  
Use data-driven tactics and strategies to predict consumer demand and optimize price and capacity/inventory availability to maximize revenue or profit. Explore demand analytics and revenue management techniques, produce rigorous and practical insights, and develop data analysis and optimization skills, based on business cases, datasets, and exercises. Examine diverse contexts and industries, such as conventional retailing, e-commerce, airline/hotel, and contemporary platform and cloud services (e.g., Uber, Dropbox, Netflix, AWS), by utilizing software (Excel, R, or Python) to build models, conduct optimization, and develop insights for a variety of revenue management examples. Only one of the following may be counted: Business Analytics 372T (Topic 24), Management Information Systems 372T (Topic 24), Operations Management 372T (Topic 24).
Same as: MIS 372T.24, O M 372T.24  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 372T.25  User Generated Content Analytics  3 Hours  

3 Lecture Hours  0 Lab Hours  
Focus on a gamut of questions ranging from strategic to operational matters pertaining to a firm's social media initiatives, metrics to capture relevant outcomes, and predictive analysis to link social media chatter to business performance. Only one of the following may be counted: Business Analytics 372 (Topic 4), 372T (Topic 25), Management Information Systems 372T (Topic 25), 373 (Topic 25).
Same as: MIS 372T.25  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 372T.26  Data Analytics for Marketing  3 Hours  

3 Lecture Hours  0 Lab Hours  
Introduction to making more effective marketing decisions through the use of data. Examine sources, collecting and cleansing methods, conducting analyses, and presenting of data. Use real-world data and applications from a variety of industries. Only one of the following may be counted: Business Analytics 372 (Topic 9), 372T (Topic 26), Marketing 354 (Topic 26), 372 (Topic 23).
Same as: MKT 354.26  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division  

BAX 375  Business Analytics in Practice  3 Hours  

3 Lecture Hours  0 Lab Hours  
Examine the role of business analytics in the context of the broader operations of a firm. Explore the use of business analytics in firms, the impact on business performance, and the ways that it can enhance firm value, through a series of case studies. Three lecture hours a week for one semester.
Pre/Corequisites: Senior standing; Business Analytics 304, 305, or Management Information Systems 304; credit or registration for Business Analytics 357, 358, and one of the following: Business Administration 130G, 353, 366P, Accounting 366P, 367P, Business Analytics 366P, International Business 366P, Finance 337P, 364P, 365P, 366P, Management 347P, 366P, 367P, 369P, Management Information Systems 366P, Marketing 366P, Operations Management 366P, or Real Estate 366P.  
Restrictions: Restricted to students in a business major.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Upper Division